Optimizing lab methods for consistent rice milling analysis
Bibliographic record
Abstract
Abstract Background and Objectives Milling is one of the most important postharvest processes in rice production. Traditionally, the McGill #2 mill has been used for lab milling to assess rice milling performance as per the recommendation of the Federal Grain Inspection Service (FGIS). However, as the FGIS is phasing out the use of the McGill #2 mill, there is a need to recalibrate milling assessments for standardized milling evaluation across various labs. Thus, the objective of this study was to assess the milling performance of three laboratory mills (McGill #2, Satake, and Zaccaria mills) on the head rice yield (HRY), milled rice yield (MRY), and whiteness index (WI) of rice. Findings MRY was greater than 60%, while HRY varied between 28% and 60% over all treatments. The Satake mill demonstrated a higher MRY and is significantly different from the McGill #2 and Zaccaria mills. Similarly, the Satake mill produced the highest HRY, followed by the Zaccaria mill and the McGill #2. On the contrary, the Satake mill exhibited the lowest WI, about 0.6 and 0.9 percentage points (pp) lower than McGill #2 and the Zaccaria mill, respectively. Conclusions Rice cultivars and mill types had the most significant impact on the aforementioned explored variables. Among the mills, the Satake mill displayed the highest MRY and HRY but had the lowest WI. We recommend that the 0.4 SLC value associated with well‐milled rice be reviewed, which could perhaps lead to more HRY and impact the economic value of rice for growers. Significance and Novelty This study offers insights into the milling capabilities of the evaluated mills. However, further research is necessary to understand and optimize other modern laboratory mills.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".